IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i4p1164-1174.html
   My bibliography  Save this article

Local Influence for Generalized Linear Models with Missing Covariates

Author

Listed:
  • Xiaoyan Shi
  • Hongtu Zhu
  • Joseph G. Ibrahim

Abstract

No abstract is available for this item.

Suggested Citation

  • Xiaoyan Shi & Hongtu Zhu & Joseph G. Ibrahim, 2009. "Local Influence for Generalized Linear Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 65(4), pages 1164-1174, December.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:4:p:1164-1174
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01179.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jansen, Ivy & Hens, Niel & Molenberghs, Geert & Aerts, Marc & Verbeke, Geert & Kenward, Michael G., 2006. "The nature of sensitivity in monotone missing not at random models," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 830-858, February.
    2. John Copas & Shinto Eguchi, 2005. "Local model uncertainty and incomplete‐data bias (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(4), pages 459-513, September.
    3. Hens, Niel & Aerts, Marc & Molenberghs, Geert & Thijs, Herbert & Verbeke, Geert, 2005. "Kernel weighted influence measures," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 467-487, March.
    4. Hong‐Tu Zhu & Sik‐Yum Lee, 2001. "Local influence for incomplete data models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 111-126.
    5. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz, 1999. "Monte Carlo EM for Missing Covariates in Parametric Regression Models," Biometrics, The International Biometric Society, vol. 55(2), pages 591-596, June.
    6. John Copas & Shinto Eguchi, 2001. "Local sensitivity approximations for selectivity bias," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 871-895.
    7. Ivy Jansen & Geert Molenberghs & Marc Aerts & Herbert Thijs & Kristel Van Steen, 2003. "A Local Influence Approach Applied to Binary Data from a Psychiatric Study," Biometrics, The International Biometric Society, vol. 59(2), pages 410-419, June.
    8. Geert Verbeke & Geert Molenberghs & Herbert Thijs & Emmanuel Lesaffre & Michael G. Kenward, 2001. "Sensitivity Analysis for Nonrandom Dropout: A Local Influence Approach," Biometrics, The International Biometric Society, vol. 57(1), pages 7-14, March.
    9. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    10. Paul Gustafson, 2001. "On measuring sensitivity to parametric model misspecification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(1), pages 81-94.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joseph Ibrahim & Geert Molenberghs, 2009. "Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 1-43, May.
    2. Wenceslao González-Manteiga & Rosa Crujeiras, 2013. "An updated review of Goodness-of-Fit tests for regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 361-411, September.
    3. Giménez, Patricia & Galea, Manuel, 2013. "Influence measures on corrected score estimators in functional heteroscedastic measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 1-15.
    4. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    5. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    6. Heng Chen & Daniel F. Heitjan, 2022. "Analysis of local sensitivity to nonignorability with missing outcomes and predictors," Biometrics, The International Biometric Society, vol. 78(4), pages 1342-1352, December.
    7. Manuel Galea & Patricia Giménez, 2019. "Local influence diagnostics for the test of mean–variance efficiency and systematic risks in the capital asset pricing model," Statistical Papers, Springer, vol. 60(1), pages 293-312, February.
    8. Niansheng Tang & Sy-Miin Chow & Joseph G. Ibrahim & Hongtu Zhu, 2017. "Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 875-903, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. S. Eftekhari Mahabadi & M. Ganjali, 2012. "An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2327-2348, July.
    2. Niansheng Tang & Sy-Miin Chow & Joseph G. Ibrahim & Hongtu Zhu, 2017. "Bayesian Sensitivity Analysis of a Nonlinear Dynamic Factor Analysis Model with Nonparametric Prior and Possible Nonignorable Missingness," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 875-903, December.
    3. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.
    4. Ivy Jansen & Geert Molenberghs, 2008. "A flexible marginal modelling strategy for non‐monotone missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 347-373, April.
    5. O'Hara Hines, R.J. & Hines, W.G.S., 2007. "Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5537-5546, August.
    6. Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    7. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
    8. de Luna, Xavier & Lundin, Mathias, 2009. "Sensitivity analysis of the unconfoundedness assumption in observational studies," Working Paper Series 2009:12, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    9. Xie, Hui, 2012. "Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1287-1300.
    10. Caroline Beunckens & Cristina Sotto & Geert Molenberghs & Geert Verbeke, 2009. "A multifaceted sensitivity analysis of the Slovenian public opinion survey data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 171-196, May.
    11. Kano, Yutaka & Takai, Keiji, 2011. "Analysis of NMAR missing data without specifying missing-data mechanisms in a linear latent variate model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1241-1255, October.
    12. Samiran Sinha & Krishna K. Saha & Suojin Wang, 2014. "Semiparametric approach for non-monotone missing covariates in a parametric regression model," Biometrics, The International Biometric Society, vol. 70(2), pages 299-311, June.
    13. Hongtu Zhu & Joseph G. Ibrahim & Xiaoyan Shi, 2009. "Diagnostic Measures for Generalized Linear Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 686-712, December.
    14. David Todem & KyungMann Kim & Jason Fine & Limin Peng, 2010. "Semiparametric regression models and sensitivity analysis of longitudinal data with non‐random dropouts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(2), pages 133-156, May.
    15. John Copas & Shinto Eguchi, 2010. "Likelihood for statistically equivalent models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(2), pages 193-217, March.
    16. Xavier de Luna & Mathias Lundin, 2014. "Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1767-1784, August.
    17. D. Todem & J. Fine & L. Peng, 2010. "A Global Sensitivity Test for Evaluating Statistical Hypotheses with Nonidentifiable Models," Biometrics, The International Biometric Society, vol. 66(2), pages 558-566, June.
    18. Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
    19. Trias Wahyuni Rakhmawati & Geert Molenberghs & Geert Verbeke & Christel Faes, 2016. "Local influence diagnostics for incomplete overdispersed longitudinal counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1722-1737, July.
    20. Ivy Jansen & Ann Van den Troost & Geert Molenberghs & Ad A. Vermulst & Jan R. M. Gerris, 2006. "Modeling Partially Incomplete Marital Satisfaction Data," Sociological Methods & Research, , vol. 35(1), pages 113-136, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:biomet:v:65:y:2009:i:4:p:1164-1174. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.